Investigating genetic overlap between antidepressant and lithium response and treatment resistance in major depressive disorder

Treatment response and resistance in major depressive disorder (MDD) are suggested to be heritable. Due to significant challenges in defining treatment-related phenotypes, our understanding of their genetic bases is limited. This study aimed to derive a stringent definition of treatment resistance and to investigate genetic overlap between treatment response and resistance in MDD. Using electronic medical records on the use of antidepressants and electroconvulsive therapy (ECT) from Swedish registers, we derived the phenotype of treatment-resistant depression (TRD) within ~ 4 500 individuals with MDD in three Swedish cohorts. Considering antidepressants and lithium are first-line treatment and augmentation used for MDD, respectively, we generated polygenic risk scores of antidepressant and lithium response for individuals with MDD, and evaluated their associations with treatment resistance by comparing TRD with non-TRD. Among 1 778 ECT-treated MDD cases, nearly all (94%) used antidepressants before first ECT, and the vast majority had at least one (84%) or two (61%) antidepressants of adequate duration, suggesting these MDD cases receiving ECT were resistant to antidepressants. We found that TRD cases tend to have lower genetic load of antidepressant response than non-TRD, although the difference was not significant; furthermore, TRD cases had significantly higher genetic load of lithium response (OR = 1.10–1.12 under different definitions). The results support evidence of heritable components in treatment-related phenotypes and highlight the overall genetic pro le of lithium-sensitivity in TRD. This finding further provides a genetic explanation for lithium efficacy in treating TRD.


Introduction
Major depressive disorder (MDD) is a leading cause of disability worldwide, and associated with a staggering burden for affected individuals and the society 1, 2 . Antidepressants are a rst-line treatment for MDD, and generally effective in reducing symptoms and preventing relapse 3 . However, only one-third of individuals with MDD reach complete symptom remission, while another one-third or more fail to respond to antidepressant medications 4 . When individuals with MDD experience insu cient response to rst-line antidepressants, other treatment strategies might be used to enhance treatment effect, including antidepressant combinations or switches, or augmentation therapies with lithium or atypical antipsychotics 5 . Lithium, which is primarily used to prevent mood episodes in bipolar disorder but also used to augment antidepressants, has been shown to prevent relapse and hospital re-admission in MDD 6,7 . In addition, electroconvulsive therapy (ECT) is typically recommended as second-or third-line treatment for individuals with severe MDD who are not responsive to pharmacotherapy, psychotherapy, or have an urgent need for rapid clinical improvement in mood due to psychotic symptoms or suicidality 8, 9 .
It has been hypothesized that individual variation in response to medications used to treat MDD may have a genetic component. For treatment response, the largest genome-wide association study (GWAS) to-date on antidepressant response (N = 5 218) led by the MDD working group of the Psychiatric Genomics Consortium estimated that 13.2% (95% CI = 2.2-24.2%) of variance in antidepressant response, measured by symptom remission, was explained by common genetic variants (i.e., SNP-based heritability) 10 . This study further demonstrated genetic overlap of antidepressant response with risk for schizophrenia 10 . The International Consortium on Lithium Genetics (ConLi + Gen) conducted a GWAS of lithium response in 2 563 individuals with bipolar disorder 11 . Although the SNP-based heritability was not reported, this study identi ed genetic markers associated with a region containing long non-coding RNAs 11 . Follow-up studies also suggested that higher genetic loading for certain psychiatric disorders (MDD and schizophrenia) were associated with worse lithium response in bipolar disorder, providing evidence for shared genetics between lithium response and risks for psychiatric disorders 12,13 . The term of treatment resistance is used when an individual fails to respond to adequate treatments 14 . Comparing treatment-resistant depression (TRD) and non-TRD cases, the ndings from previous studies generally supported that MDD treatment resistance has a genetic component and shares genetic risks with certain psychiatric disorders [15][16][17][18] . For example, a recent study based on UK primary care records estimated the SNP-based heritability of treatment resistance at 7.7% (95% CI = 2.4-13.0% in observed scale) and demonstrated that the polygenicity of ADHD was associated with treatment resistance among MDD 16 .
Nonetheless, signi cant challenges remain in de ning treatment resistance in psychiatric research 14,19,20 . For MDD, typically only the use of antidepressant medications is considered in de ning TRD, and the required number of failed antidepressant trials has been debated [14][15][16][17]19 . Unclear de nition of TRD makes comparisons across studies di cult and limits our understanding of biological underpinnings in MDD treatment resistance. Here, we de ned TRD based on both antidepressant and ECT use, given that ECT is indicated for individuals with severe MDD who fail to respond to rst-line treatment 21,22 . We also leveraged the latest GWAS of antidepressants and lithium response to investigate the genetic overlap between treatment response and resistance in three Swedish cohorts with over 4 500 individuals with MDD.

Materials And Methods
Data source Study population. This study consisted of case-only samples with clinical diagnoses of MDD. We extracted MDD cases from three Swedish cohorts. First, we used the Predictors for ECT (PREFECT) study that recruited individuals from the Swedish National Quality Register for ECT between 2013 and 2017 23 . From the PREFECT study, we extracted the severe MDD cases, i.e., those receiving ECT for a major depressive episode in the context of MDD, but excluding cases receiving ECT for other mood disorders like bipolar or schizoaffective disorder (N = 1 922) 24 . Second, we used the Internet-based Cognitive Behavior Therapy (iCBT) study which recruited mild-moderate MDD cases who were treated with internetbased cognitive behavior therapy (N = 964) 25 . Third, we used the population-based cohort of the Swedish Twin Studies of adults: Genes and Environment (STAGE), from which we extracted 1 686 MDD cases who either had a clinical diagnosis of MDD from the linked patient registers or ful lled DSM-IV diagnostic criteria, and had no diagnosis of schizophrenia and bipolar disorder 26 . All participants provided informed consent. The studies were approved by the Regional Ethics Review Board in Stockholm. Further details about these samples have been described previously [24][25][26] . Altogether, 4 572 MDD cases were eligible for the study.
GWAS summary statistics of antidepressant response. GWAS summary statistics of antidepressant response were obtained from the largest GWAS of antidepressant response to-date (N = 5 218 MDD cases) 10 . Two phenotypes of antidepressant response were assessed in the GWAS. The rst phenotype de ned remission as a binary trait, i.e., "whether depressive symptom score in the rating scale decreases to a pre-speci ed threshold after antidepressant use" ("remission"). The second phenotype was a quantitative trait, de ned as "the percentage change of symptoms scale after antidepressant use" (referred as "percentage improvement"), with a higher percentage improvement indicating a better treatment response. Since the signi cant SNP-based heritability was reported only for the binary "remission" phenotype 10 , we used the summary statistics of this phenotype in all analyses.
GWAS summary statistics of lithium response. GWAS summary statistics of lithium response were obtained from the largest GWAS on lithium response in patients with bipolar disorder (N = 2 563) conducted by the ConLi + Gen Consortium 11,27 . Similar to antidepressant response GWAS, two phenotypes of lithium response were assessed: a binary trait dichotomized by a pre-de ned cutoff of the rating scale (good vs. poor response to lithium treatment) and a quantitative measure of symptom improvement; here we also used summary statistics of the binary trait and based on samples with European ancestry (N = 2 343) in all analyses.
GWAS summary statistics of psychiatric disorders. We also obtained GWAS summary statistics of corresponding psychiatric disorders (MDD and bipolar disorder) from the latest published GWAS (N = 500 199 in MDD GWAS and N = 51 710 in bipolar disorder GWAS) for sensitivity analyses 28, 29 .
Phenotype de nitions TRD. We derived TRD de nitions in the PREFECT samples who have received ECT in the context of MDD.
Repeated treatment failure is one frequent reason for administering ECT in individuals with MDD 21, 22 , but ECT is also used as the rst-line treatment in cases of severe psychotic symptoms or life-threatening conditions 30 . Therefore, we utilized the prescription records of antidepressants before the rst ECT treatment to ensure our de nitions capture actual TRD cases, i.e., individuals with MDD who have received ECT due to treatment resistance. The drug prescription records were obtained from the Swedish National Prescription Drug Register (PDR, available between July 2005 to May 2018) 24 , using Anatomical Therapeutic Chemical (ATC) codes under the category of "N06A" for antidepressants and N05AN01 for lithium. We de ned TRD cases in the PREFECT samples as those who have used at least one ("narrow_1" TRD de nition) or two different antidepressants ("narrow_2" TRD) of adequate duration before the rst ECT treatment. The treatment duration for each antidepressant was calculated based on the rst and last dispense date of the antidepressant in the same treatment period (i.e., the time interval between two consecutive prescriptions for the same antidepressant within 120 days 31 ). Similar to other studies, we considered adequate treatment duration of ≥ 6 consecutive weeks in order to account for the expected length of therapeutic effect and to distinguish from drug switches due to adverse effects 16, 32 . For comparison, we also considered a "broad de nition" without restriction for antidepressant use before the rst treatment of ECT.
Non-TRD. The comparison group of non-TRD cases was derived from iCBT and STAGE samples. To match the broad de nition of TRD, we considered those without ECT as non-TRD. Since the iCBT samples were mild-moderate MDD cases recruited to begin internet-based cognitive behavior therapy 25 , these samples were unlikely to be treated with ECT. For the STAGE samples, we obtained medication data from the PDR to de ne adequate antidepressant duration similarly to our de nition in TRD. These STAGE samples were also linked with the patient register from which we identi ed 19 individuals who had received ECT. In STAGE, non-TRD cases were de ned as MDD cases who have used antidepressants but with no more than two antidepressants of adequate duration (≥ 6 weeks), and have never used ECT. This de nition was chosen to capture likely antidepressant responders and to correspond with the commonly used cut-off in the literature for de ning TRD/non-TRD cases 19 .
Taken together, we derived three sets of comparisons, with one broad de nition based on ECT use only and two narrow de nitions integrating both ECT and antidepressant use: 1. Broad: MDD cases receiving ECT treatment (TRD), compared with those without ECT (non-TRD); 2. Narrow_1: MDD cases with ≥ 1 antidepressant of adequate duration before the rst ECT (TRD), compared with those with ≤ 2 antidepressants of adequate duration and without ECT (non-TRD); 3. Narrow_2: MDD cases with ≥ 2 different antidepressants of adequate duration before the rst ECT (TRD), compared with those with ≤ 2 antidepressants of adequate duration and without ECT (non-TRD).

Genotyping, quality control and imputation
Genotyping was conducted at Life and Brain GmbH (Bonn, Germany) for both PREFECT and iCBT samples, using Illumina In nium Global Screening Arrays (v1) 24,25 . Samples from STAGE were genotyped with the same array by the SNP&SEQ Technology Platform (Uppsala, Sweden) 26 .
After harmonizing the markers and allele coding, we merged raw genotype data from the three studies. Before merging, we excluded SNPs from each study for the following reasons: monomorphic sites; indels; strand ambiguous; minor allele frequency (MAF) < 0.01. In the merged data, 4 187 MDD cases had both genotypes and phenotypes. We then used the PGC Ricopili pipeline for quality control (QC) 33 . After rst removing SNPs with missingness > 5%, we removed 14 samples (0.33%) due to any of the following criteria: per-sample call rate < 0.98; excessive heterozygosity (FHET outside +/-0.20); or sex mismatch. We excluded SNPs due to any of the following: per-SNP call rate < 0.98; invariant; Hardy-Weinberg disequilibrium (P < 1e − 6 in controls and cases separately); difference in call rate between cases and controls > 0.01; MAF < 0.01. We retained 459 906 SNPs (92.11%) after QC. By projecting the rst two principal components (PCs) of the study samples to the reference panel of the 1000 Genome global population, we identi ed and excluded 56 (1.34%) non-European ancestral outliers whose rst two PCs exceeded 6 standard deviations from the mean values of the European samples in the reference population. Relatedness was estimated from the genotype data and one in each pair of related individuals (75 pairs with > 0.2; is estimated proportion of the genome shared identical-by-descent) was excluded.
After QC, Sanger imputation service was used to impute genotype data to the reference panel of Haplotype Reference Consortium data (HRC1.1). EAGLE2 and IMPUTE2 were used for pre-phasing and imputation, respectively [34][35][36] .

Statistical analyses
Polygenic risk scores (PRS). Before calculating the PRS, we further excluded the following SNPs from GWAS summary statistics: 1) SNPs with MAF < 0.1 or INFO score < 0.9; 2) duplicate SNPs; 3) strandambiguous SNPs; 4) SNPs in major histocompatibility complex regions (chr6:28-34 Mb). SNPs overlapping with Hapmap3 were extracted and the summary statistics were rescaled to account for linkage disequilibrium (LD) in SBayesR, which is a state-of-the-art method with high prediction accuracy in psychiatric disorders 37,38 . Finally, PRS was calculated in imputed data for each individual as the sum of the number of risk alleles weighted by allelic effects in PLINK (version 2.0) 39 ; and standardised within the whole sample. We also calculated the PRS of MDD and bipolar disorder in the same way for sensitivity analyses.
Association analysis. To examine the genetic association of treatment response with TRD status, we rst tested the mean differences in the PRS of antidepressant or lithium response among TRD cases compared to non-TRD cases (t-test). Logistic regression was used to estimate the odds ratios (OR) corresponding to per standard deviation (SD) increase in the PRS of antidepressant or lithium response, adjusting for the rst four principal components (PCs). We ran these models in all phenotype comparisons. The proportion of variance in MDD treatment resistance explained by PRS (Nagelkerke's R 2 ) was calculated by comparing the full model including both PRS and covariates to the baseline model only including covariates. We converted Nagelkerke's R 2 to the liability scale by assuming 10% MDD cases meeting our stringent de nition of TRD. We tested the trend of association in PRS quartiles with treatment resistance using Chi-squared test.
We employed a Bonferroni-corrected p-value threshold for statistical signi cance, controlling for multiple testing across six comparisons (two PRS of treatment response in three sets of TRD/non-TRD comparisons; P ≤ 0.05/6 = 0.008). All analyses were conducted in R (Version 4.0.0) 40 .
Sensitivity analysis. To account for potential genetic overlap between treatment response and corresponding psychiatric disorder risk (MDD and bipolar disorder) 12, 41 , we additionally adjusted for the PRS of MDD and bipolar disorder in the main models.

π ˆ π
To examine whether the results were driven by lithium use in TRD patients, we further excluded patients with lithium use, and estimated the association between PRS of lithium response and treatment resistance in MDD.

Results
After QC, 1 778 MDD cases who had received ECT and 2 264 without ECT were available for analyses. We further utilized the prescription records of antidepressants before the rst ECT treatment to de ne TRD. Among the MDD cases with ECT ("broad TRD" de nition), 1 674 cases (94.2%) had used antidepressants before the rst ECT. The vast majority of these cases-1 487 (83.6%) and 1 081 (60.8%), respectively-had at least one or two different antidepressants of adequate duration ("narrow_1 TRD" and "narrow_2 TRD"), suggesting that these MDD cases receiving ECT were resistant to antidepressants. Among the 2 264 MDD cases without ECT ("broad non-TRD"), 1 483 (66.5%) had no more than two antidepressants of adequate duration ("narrow non-TRD") ( Table 1).  To investigate the genetic overlap between treatment response and resistance in MDD, we calculated the PRS of antidepressants and lithium response, and evaluated their associations with treatment resistance by comparing the PRS burdens among TRD and non-TRD cases.
With the polygenic component underlying antidepressants response 10 , we would expect an inverse association between the PRS of antidepressants response and treatment resistance in MDD. For the broad and narrow_1 de nitions, the results were in the expected effect direction, i.e., TRD cases had lower PRS of antidepressants response compared with non-TRD cases, although the mean differences were not signi cant (Table 1). Under these two de nitions, per SD increase in the PRS of antidepressants response had a slightly reduced risk of TRD (e.g., OR = 0.98, 95% CI = 0.92-1.06, P = 0.67 under the narrow_1 de nition; Table S1). However, the effect direction was inconsistent in the narrow_2 de nition, potentially due to the reduced sample size in this most stringent de nition. The results remained unchanged after further adjusting for the PRS of MDD (Table S1).
Given that lithium is recommended as an augmentation therapy for MDD patients who have experienced insu cient response to rst-line antidepressants, we further tested the association of the lithium response PRS with MDD treatment resistance. TRD cases had signi cantly higher PRS of lithium response than non-TRD cases (mean difference = 0.11, P = 0.004 under narrow_1 de nition; Table 1). We found signi cant associations between PRS of lithium response and all TRD de nitions in the logistic regression model, with per SD increase in the lithium response PRS associated with OR = 1.12 (95%CI = 1.04-1.20, P = 0.003 under the narrow_1 de nition; Fig. 1A, Table S1), although the variance explained was small (Fig. 1B). Further adjustment for additional PRS of MDD, bipolar disorder, or both, did not change the estimates. Similar results were observed after excluding TRD cases with lithium use (N = 502, 454 and 357 under broad, narrow_1 and narrow_2 de nitions), suggesting that the association was not due to effect of lithium use among TRD cases (Table S2). To further quantify the effect of polygenic load of lithium response among MDD, we divided the MDD cases into quartiles based on their PRS of lithium response, and observed a clear trend of higher proportion of TRD cases in the higher PRS quartiles (P trend <0.005 in all de nitions; Fig. 1C). Taken together, the results suggested that TRD cases have higher polygenic load of responding to lithium compared to the non-TRD cases.

Discussion
Leveraging the latest GWAS of treatment response and unique resources of clinically ascertained cohorts with comprehensive treatment records, we investigated whether polygenic load of treatment response differ between TRD and non-TRD cases. We found that compared to non-TRD, patients with TRD tend to have lower genetic load of antidepressant response and signi cantly higher genetic load of lithium response. These results provide evidence for the genetic overlap of treatment response with treatment resistance in MDD and reveal an overall genetic pro le of antidepressant non-responsiveness and lithiumsensitivity in patients with TRD.
To the best of our knowledge, this is the rst study using the detailed treatment records of both antidepressants and ECT to derive de nitions of TRD and non-TRD. Recent reviews highlighted the key outstanding issue in the research of psychiatric treatment resistance regarding its de nition 14,19,20 . Indeed, three core components-correct diagnosis, adequate treatment, and non-response-are required to establish treatment resistance 14 . Data from clinical trials are useful for this purpose; however, there is a paucity of clinical trials with su cient size and adequate length of follow-up. Recently, emerging efforts utilizing Electronic Health Records provide an exciting opportunity to study treatment response and resistance in large-scale, real-world healthcare settings 16, 42 . Using comprehensive clinical records from the Swedish healthcare registers, our work extends these efforts and further demonstrates that integrating the information of ECT use can provide a useful TRD de nition. ECT is used to treat individuals with TRD 21,22 , and we also used antidepressant prescription records to con rm that these individuals had multiple trials of adequate antidepressant drugs prior to ECT. Notably, our TRD de nitions based on ECT and antidepressant use are more stringent and likely to ascertain more severe patients with TRD compared to those ful ll common TRD de nitions based on failed antidepressants trials. One key advantage of using a stringent de nition is to minimize misclassi cation between TRD and non-TRD groups which is common in the categorical-based de nitions with certain cutoffs 43 . In addition, ascertaining severe TRD samples can be useful in particular for research purposes, e.g., extreme phenotyping has been shown as a powerful strategy for novel genetic discoveries 24,44 . Finally, our de nitions t better with the recently proposed concept of "di cult to treat depression" (DTD). The DTD concept differs from the conventional TRD concept in that it moves away from the exclusive focus on acute symptomatic response and instead aims to reduce depression burden despite usual treatment efforts 20,45,46 .
Our work extended previous genetic studies of treatment response and further demonstrated the genetic overlap between treatment response and resistance in MDD. As expected, TRD cases appeared to have lower genetic load of responding to antidepressants compared with non-TRD, although the mean difference was non-signi cant. Comparing results under different de nitions, it appeared that the "broad" and "narrow_1" de nitions were capable of distinguishing between TRD and non-TRD cases. Similar results were found in the Generation Scotland study (177 TRD vs. 2,455 non-TRD; expected effect direction but also non-signi cant) using TRD de nition with at least two switches between antidepressants 10 , con rming that our stringent de nitions yielded consistent ndings as other de nitions commonly used in the literature. Further comparisons across different TRD de nitions are warranted.
Lithium is known to be effective as an augmentation for patients with TRD 5, 47 . Our novel nding based on PRS, where TRD cases had notably higher genetic load to respond to lithium than non-TRD cases, adds to the evidence of lithium e cacy in treating TRD and further demonstrates that its effect is underpinned by genetic mechanisms. The mechanisms of action of lithium are unclear; however, earlier hypothesis suggested that it plays a role in 5-HT neurotransmission, and SNPs in the 5-HT transporter gene have been correlated with response to lithium in patients with depression 12, 48-51 . If the results are replicated, this nding will motivate future genetic studies to reveal mechanisms of actions involved in lithium.
This study features a stringent measure of MDD treatment resistance, along with the use of the largest GWAS to-date of treatment responses and unique clinical cohorts to investigate the genetics of treatment response and resistance in MDD. We examined response to antidepressants and lithium, but could not evaluate antipsychotics response here. Similar to lithium, atypical antipsychotics are also recommended as augmentation in MDD patients non-responding to antidepressants 52 . Previous GWAS have been conducted on antipsychotic response or resistance, although few speci cally targeted the atypical antipsychotics used in MDD and with su cient sample size 53,54 . However, given that the effect of antipsychotic augmentation in TRD is similar to that of lithium 55 , we might expect similar ndings for antipsychotic response. In addition, we lacked data on psychotherapies and other nonpharmacotherapies to integrate them in our TRD de nition. This is a common issue when de ning treatment resistance in psychiatric disorders 20 . But given that psychotherapy is used as rst-line (before or combined with antidepressant use), it is less likely to impact our stringent TRD de nition based on ECT use. Further efforts to incorporate multiple therapies are needed to re ne treatment resistance phenotype 9 .
In summary, we derived a stringent TRD de nition based on antidepressant and ECT use to capture severe MDD with treatment resistance. Our results highlighted that patients with TRD have signi cantly higher genetic load of lithium response compared to non-TRD. This nding forms a genetic explanation for the effectiveness of lithium in treating TRD patients. Figure 1 The